public class CopyOp extends BaseTransformOp
extraArgs, extraArgz, n, numProcessed, passThrough, x, xVertexId, y, yVertexId, z, zVertexId
dimensions, inPlace, sameDiff, scalarValue
Constructor and Description |
---|
CopyOp() |
CopyOp(INDArray x) |
CopyOp(INDArray x,
INDArray z) |
CopyOp(INDArray x,
INDArray xDup,
INDArray z) |
CopyOp(INDArray x,
INDArray y,
INDArray z,
long n) |
CopyOp(INDArray x,
INDArray z,
long n) |
CopyOp(SameDiff sameDiff) |
CopyOp(SameDiff sameDiff,
SDVariable i_v,
boolean inPlace) |
CopyOp(SameDiff sameDiff,
SDVariable i_v,
int[] shape,
boolean inPlace,
Object[] extraArgs) |
CopyOp(SameDiff sameDiff,
SDVariable i_v,
Object[] extraArgs) |
CopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2) |
CopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
boolean inPlace) |
CopyOp(SameDiff sameDiff,
SDVariable i_v1,
SDVariable i_v2,
Object[] extraArgs) |
Modifier and Type | Method and Description |
---|---|
List<SDVariable> |
doDiff(List<SDVariable> f1)
The actual implementation for automatic differentiation.
|
void |
exec()
Execute the op if its pass through (not needed most of the time)
|
boolean |
isPassThrough()
Returns whether the op should be executed or not (through the executioner)
|
String |
onnxName()
The opName of this function in onnx
|
String |
opName()
The name of the op
|
int |
opNum()
The number of the op (mainly for old legacy XYZ ops
like
Op ) |
String |
tensorflowName()
The opName of this function tensorflow
|
calculateOutputShape, opType, z
equals, exec, extraArgs, extraArgsBuff, extraArgsDataBuff, getOpType, hashCode, init, initFromOnnx, initFromTensorFlow, isExecSpecial, n, numProcessed, outputVariables, setN, setX, setY, setZ, toCustomOp, toString, x, y
arg, args, asProperties, attributeAdaptersForFunction, configFieldName, diff, dup, f, getValue, hasPlaceHolderInputs, isConfigProperties, larg, mappingsForFunction, onnxNames, outputVariables, propertiesForFunction, rarg, resolvePropertiesFromSameDiffBeforeExecution, setInstanceId, setValueFor, tensorflowNames
clone, finalize, getClass, notify, notifyAll, wait, wait, wait
exec, extraArgs, extraArgsBuff, extraArgsDataBuff, init, isExecSpecial, n, numProcessed, setExtraArgs, setN, setX, setY, setZ, toCustomOp, x, y
public CopyOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2)
public CopyOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace)
public CopyOp(SameDiff sameDiff)
public CopyOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, Object[] extraArgs)
public CopyOp(SameDiff sameDiff, SDVariable i_v, boolean inPlace)
public CopyOp(SameDiff sameDiff, SDVariable i_v, int[] shape, boolean inPlace, Object[] extraArgs)
public CopyOp(SameDiff sameDiff, SDVariable i_v, Object[] extraArgs)
public CopyOp()
public CopyOp(INDArray x)
public int opNum()
DifferentialFunction
Op
)opNum
in interface Op
opNum
in class DifferentialFunction
public String opName()
DifferentialFunction
opName
in interface Op
opName
in class DifferentialFunction
public String onnxName()
DifferentialFunction
onnxName
in class DifferentialFunction
public String tensorflowName()
DifferentialFunction
tensorflowName
in class DifferentialFunction
public void exec()
Op
public boolean isPassThrough()
Op
isPassThrough
in interface Op
isPassThrough
in class BaseOp
public List<SDVariable> doDiff(List<SDVariable> f1)
DifferentialFunction
doDiff
in class DifferentialFunction
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